Computers are very effective at storing large amounts of data, such as in a database. Over the last half century or so, techniques have been refined for establishing computational options, such as accessing or querying the stored data, viewing the data, modifying the data, etc. In these scenarios, the data can be thought of as relatively static and so the techniques, such as database querying techniques tend not to be very applicable to time sensitive scenarios, such as those involving real-time or near real-time. For instance, a database query technique designed to retrieve a definition of a word from a dictionary database need not be time sensitive since the data is statically stored in the database.
In contrast, other scenarios tend to involve streaming data in real-time or near real-time. For instance, a temperature sensor may be configured to periodically output a time-stamped signal corresponding to a sensed temperature. When viewed collectively this output can be thought of as a stream of data or a data stream. The above mentioned database querying techniques are not generally applicable in the data stream scenarios. Instead, stream processing techniques have been developed for use with data streams.
Stream processing techniques offer much more limited computational options than those available in traditional database scenarios. Stated another way, a very small set of computations can presently be performed with stream processing. The present concepts introduce new stream processing techniques that greatly increase the set of computations that can be accomplished with stream processing.